Extreme Gradient Boosting: A Machine Learning Technique for Daily Global Solar Radiation Forecasting on Tilted Surfaces | Journal of Engineering Sciences

Extreme Gradient Boosting: A Machine Learning Technique for Daily Global Solar Radiation Forecasting on Tilted Surfaces

Author(s): Mbah O. M.1*, Madueke C. I.2, Umunakwe R.2, Agba M. N.3

Affiliation(s):
1 Department of Mechanical Engineering, Federal University Oye-Ekiti, Ikole, City, 370105, Ekiti-State, Nigeria;
2 Department of Material and Metallurgical Engineering, Federal University Oye-Ekiti, Street, Ikole, 370105, Ekiti -State, Nigeria;
3 Department of System Engineering, University of Lagos, Street, Akoka, 100213, Lagos State, Nigeria

*Corresponding Author’s Address: [email protected]

Issue: Volume 9, Issue 2 (2022)

Dates:
Submitted: August 13, 2022
Accepted for publication: October 27, 2022
Available online: November 2, 2022

Citation:
Mbah O. M., Madueke C. I., Umunakwe R., Agba M. N. (2022). Extreme gradient boosting: A machine learning technique for daily global solar radiation forecasting on tilted surfaces. Journal of Engineering Sciences, Vol. 9(2), pp. E1-E6, doi: 10.21272/jes.2022.9(2).e1

DOI: 10.21272/jes.2022.9(2).e1

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. Enhancing solar irradiance and accurate forecasting is required for improved performance of photovoltaic and solar thermal systems. In this study, Extreme Gradient Boosting (XGBoost) model was developed using three input parameters (time, day number, and horizontal solar radiation) and was utilized to forecast daily global solar radiation on tilted surfaces. The proposed model was built using XGBRegressor with five generations, 100 n estimators, and a learning rate of 0.1. Three statistical metrics, such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to compare the model’s results to observed solar radiation data from the Nation Centre for Energy, Research and Development, University of Nigeria, Nsukka. The results showed improved prediction accuracy and XGBoost capability to estimate daily global solar radiation on tilted surfaces. In the training section, the proposed model had a statistical performance of R2 = 0.9977, RMSE = 1.6988, and MAE = 1.081, and in the testing section, R2 = 0.9934, RMSE = 2.8558, and MAE = 2.033. XGBoost model demonstrated a better performance when compared with other models in the literature. As a result, the proposed model provides an effective approach for estimating solar radiation.

Keywords: machine learning model, extreme gradient boosting, solar radiation prediction.

References:

  1. Halawa, E., Ghaffarian Hoseini, A., Li, D. H. W. (2014). Empirical correlations as a means for estimating monthly average daily global radiation: a critical overview. Renewable Energy, Vol. 72, pp. 149-153.
  2. Besharat, F., Dehghan, A. A., Faghih, A. R. (2013). Empirical models for estimating global solar radiation: A review and case study. Renewable and Sustainable Energy Reviews, Vol. 21, pp. 798-821.
  3. Pinker, R. T., Frouin, R., Li, Z. (1995). A review of satellite methods to derive surface shortwave irradiance. Remote Sensing of Environment, Vol. 51, pp. 108-124.
  4. Hansen, J. W. (1999). Stochastic daily solar irradiance for biological modeling applications. Agricultural and Forest Meteorology, Vol. 94, pp. 53-63.
  5. Chen, J.-L., Li, G.-S. (2013). Estimation of monthly average daily solar radiation from measured meteorological data in Yangtze River Basin in China. International Journal of Climatology, Vol. 33, pp. 487-498.
  6. Wu, G., Liu, Y., Wang, T. (2007). Methods and strategy for modeling daily global solar radiation with measured meteorological data – A case study in Nanchang station, China. Energy Conversion and Management, Vol. 48, pp. 2447-2452.
  7. Mbah, O. M., Mgbemene, C. A., Enibe, S. O., Ozor, P. A., Mbohwa, C. (2018). Comparison of experimental data and isotropic sky models for global solar radiation estimation in Eastern Nigeria. World Congr. Eng,, Vol. 2, pp. 4-8.
  8. Mbah, O. M., Ozor, P., Mgbemene, C., Enibe, S. O., Mbohwa, C. (2018). Comparative analysis of anisotropic sky models and experimental data in estimating solar radiation on tilted surface in Sub-Saharan African climate. IEOM Conference. IEOM 2018, 2018.
  9. Chabane, F., Arif, A., Moummi, N., Brima, A. (2020). Prediction of Solar Radiation According to Aerosol Optical Depth. Iranian (Iranica) Journal of Energy & Environment, Vol. 11, pp. 271-276.
  10. Herath, H., Ariyathunge, S., Karunasena, G. (2021). Development of a Mathematical Model to Forecast Solar Radiation and Validating Results Using Machine Learning Technique. European PMC, Research Square, https://doi.org/ 10.21203/rs.3.rs-669429/v1
  11. Shourehdeli, S. A., Mobini, K., Asakereh, A. (2022). Modeling of Isentropic Coefficients Used in One Dimensional Model to Predict Ejector Performance at Critical Mode. Iranian (Iranica) Journal of Energy & Environment, Vol. 13, pp. 111-123.
  12. Olatomiwa, L., Mekhilef, S., Shamshirband, S., Petković, D. (2015). Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renewable and Sustainable Energy Reviews, Vol. 51, pp. 1784-1791.
  13. Hacioğlu, R. (2017). Prediction of solar radiation based on machine learning methods. The Journal of Cognitive Systems, Vol. 2, pp. 16-20.
  14. Guermoui, M., Rabehi, A., Gairaa K., Benkaciali, S. (2018). Support vector regression methodology for estimating global solar radiation in Algeria. The European Physical Journal Plus, Vol. 133, pp. 1-9.
  15. Chen, J.-L., Li, G.-S. (2014). Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables. Theoretical and Applied Climatology, Vol. 115, pp. 627-638.
  16. Chen, J.-L., Li, G.-S., Wu, S.-J. (2013). Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration. Energy Conversion and Management, Vol. 75, pp. 311-318, 2013.
  17. Chen, J.-L., Liu, H.-B., Wu, W., Xie, D.-T. (2011). Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study. Renewable Energy, Vol. 36, pp. 413-420.
  18. Benmouiza, K., Cheknane, A. (2013). Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models. Energy Conversion and Management, Vol. 75, pp. 561-569.
  19. Motameni, H. (2020). Determining the composition functions of Persian non-standard sentences in terminology using a deep learning fuzzy neural network model. International Journal of Engineering, Vol. 33, pp. 2471-2481.
  20. Mahdavi Jafari, M., Khayati, G. R., Hosseini, M., Danesh-Manesh, H. (2017). Modeling and optimization of roll-bonding parameters for bond strength of Ti/Cu/Ti clad composites by artificial neural networks and genetic algorithm. International Journal of Engineering, Vol. 30, pp. 1885-1893.
  21. Rahimikhoob, A. (2010). Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renewable Energy, Vol. 35, pp. 2131-2135.
  22. Marzo, A., Trigo-Gonzalez, M., Alonso-Montesinos, J., Martı́nez-Durbán, M., López, G., Ferrada, P., Fuentealba, E., Cortés, M., Batlles, F. J. (2017). Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation. Renewable Energy, Vol. 113, pp. 303-311.
  23. Torabi, M., Mosavi, A., Ozturk, P., Varkonyi-Koczy, A., Istvan, V. (2018). A hybrid machine learning approach for daily prediction of solar radiation. International Conference on Global Research and Education, 2018.
  24. Gala, Y., Fernández, Á., Dı́az, J., Dorronsoro, J. R. (2016). Hybrid machine learning forecasting of solar radiation values. Neurocomputing, Vol. 176, pp. 48-59.
  25. Achour, L., Bouharkat, M., Assas, O., Behar, O. (2017). Hybrid model for estimating monthly global solar radiation for the Southern of Algeria : (Case study: Tamanrasset, Algeria). Energy, Vol. 135, pp. 526-539.
  26. Quej, V. H., Almorox, J., Arnaldo, J. A., Saito, L. (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 155, pp. 62-70.
  27. Ağbulut, Ü., Gürel, A. E., Ergün, A., Ceylan, İ. (2020). Performance assessment of a V-Trough photovoltaic system and prediction of power output with different machine learning algorithms. Journal of Cleaner Production, Vol. 268, 122269.
  28. Ağbulut, Ü., Gürel, A. E., Biçen, Y. (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, Vol. 135, 110114.
  29. Rabehi, A., Guermoui, M., Lalmi, D. (2020). Hybrid models for global solar radiation prediction: A case study. International Journal of Ambient Energy, Vol. 41, pp. 31-40.
  30. Mbah, O. M., Madueke, C. I., Umunakwe, R., Okofor, C. O. (2022). Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction. Journal of Engineering Sciences, Vol. 9(2), G1-G6, https://doi.org/10.21272/jes.2022.9(2).e1
  31. Feng, Y., Gong, D., Zhang, Q., Jiang, S., Zhao, L., Cui, N. (2019). Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation. Energy Conversion and Management, Vol. 198, 111780.

Full Text




© 2014-2024 Sumy State University
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights are reserved by SumDU